Merge branch 'main' into text2igm

app-controlnet.py

@@ -107,10 +107,8 @@ compel_proc = Compel(
     truncate_long_prompts=False,
 )
 if TORCH_COMPILE:
-    pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune", fullgraph=False)
-    pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune", fullgraph=False)
-    pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=False)
-    pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=False)
+    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
 
     pipe(prompt="warmup", image=[Image.new("RGB", (768, 768))], control_image=[Image.new("RGB", (768, 768))])
 

app-img2img.py

@@ -79,10 +79,8 @@ if psutil.virtual_memory().total < 64 * 1024**3:
     pipe.enable_attention_slicing()
 
 if TORCH_COMPILE:
-    pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune", fullgraph=False)
-    pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune", fullgraph=False)
-    pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=False)
-    pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=False)
+    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
 
     pipe(prompt="warmup", image=[Image.new("RGB", (512, 512))])
 

app-txt2img.py

@@ -79,10 +79,8 @@ if psutil.virtual_memory().total < 64 * 1024**3:
     pipe.enable_attention_slicing()
 
 if TORCH_COMPILE:
-    pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune", fullgraph=False)
-    pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune", fullgraph=False)
-    pipe.unet = torch.compile(pipe.unet, mode="max-autotune", fullgraph=False)
-    pipe.vae = torch.compile(pipe.vae, mode="max-autotune", fullgraph=False)
+    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
 
     pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
 

latent_consistency_controlnet.py

@@ -1,3 +1,4 @@
+# from https://github.com/taabata/LCM_Inpaint_Outpaint_Comfy/blob/main/LCM/pipeline_cn.py
 # Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");